#!/usr/bin/python3
# -*- coding: utf-8 -*-

# Copyright © 2009, 2012 marmuta <marmvta@gmail.com>
#
# This file is part of Onboard.
#
# Onboard is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# Onboard is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.

import sys, re, codecs, math
import pypredict

def main():
    model = pypredict.DynamicModel()
    model.load(sys.argv[1])
    text = pypredict.read_corpus(sys.argv[2])

    word_count, ngram_count, entropy, perplexity = calc_stats(model, text)

    print "test: words %d, n-grams %d, entropy %f bit/word, perplexity %f" % \
          (word_count, ngram_count, entropy, perplexity)

def calc_stats(model, text):

    ngram_count = 0
    entropy = 0

    tokens, spans = pypredict.tokenize_text(text)
    word_count = len(tokens)

    # extract n-grams of maximum length
    for i in xrange(len(tokens)):
        b = max(i-(model.order-1),0)
        e = min(i-(model.order-1)+model.order, len(tokens))
        ngram = tokens[b:e]
        if len(ngram) != 1:
            p = model.get_probability(ngram)
            e = math.log(p,2) if p else float("infinity")
            entropy += e
            ngram_count += 1

    entropy = -entropy/word_count if word_count else 0
    try:
        perplexity = 2 ** entropy
    except:
        perplexity = 0

    return word_count, ngram_count, entropy, perplexity


if __name__ == '__main__':
    main()

